





import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__  			=logging.get_logger(__name__)






def      lowerCamelCase__      (__lowerCamelCase						):
     _SCREAMING_SNAKE_CASE							:						int         =				SwinConfig.from_pretrained(
         "microsoft/swin-tiny-patch4-window7-224",							out_features=["stage1", "stage2", "stage3", "stage4"]						)
     _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				MaskFormerConfig(backbone_config=SCREAMING_SNAKE_CASE__						)

     _SCREAMING_SNAKE_CASE							:						Optional[int]         =				"""huggingface/label-files"""
     if "ade20k-full" in model_name:
          # this should be ok
          _SCREAMING_SNAKE_CASE							:						int         =				847
          _SCREAMING_SNAKE_CASE							:						Optional[int]         =				"""maskformer-ade20k-full-id2label.json"""
     elif "ade" in model_name:
          # this should be ok
          _SCREAMING_SNAKE_CASE							:						int         =				150
          _SCREAMING_SNAKE_CASE							:						Tuple         =				"""ade20k-id2label.json"""
     elif "coco-stuff" in model_name:
          # this should be ok
          _SCREAMING_SNAKE_CASE							:						Tuple         =				171
          _SCREAMING_SNAKE_CASE							:						str         =				"""maskformer-coco-stuff-id2label.json"""
     elif "coco" in model_name:
          # TODO
          _SCREAMING_SNAKE_CASE							:						str         =				133
          _SCREAMING_SNAKE_CASE							:						int         =				"""coco-panoptic-id2label.json"""
     elif "cityscapes" in model_name:
          # this should be ok
          _SCREAMING_SNAKE_CASE							:						Tuple         =				19
          _SCREAMING_SNAKE_CASE							:						Any         =				"""cityscapes-id2label.json"""
     elif "vistas" in model_name:
          # this should be ok
          _SCREAMING_SNAKE_CASE							:						Tuple         =				65
          _SCREAMING_SNAKE_CASE							:						List[str]         =				"""mapillary-vistas-id2label.json"""

     _SCREAMING_SNAKE_CASE							:						int         =				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							repo_type="dataset"						),							"r"						)						)
     _SCREAMING_SNAKE_CASE							:						Any         =				{int(SCREAMING_SNAKE_CASE__						): v for k, v in idalabel.items()}

     return config






def      lowerCamelCase__      (__lowerCamelCase						):
     _SCREAMING_SNAKE_CASE							:						Any         =				[]
     # stem
     # fmt: off
     rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")						)
     rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")						)
     rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")						)
     rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias")						)
     # stages
     for i in range(len(config.backbone_config.depths						)						):
          for j in range(config.backbone_config.depths[i]						):
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")						)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")						)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")						)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")						)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")						)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")						)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")						)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")						)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")						)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")						)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")						)
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")						)

          if i < 3:
               rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")						)
               rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")						)
               rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")						)
          rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")						)
          rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")						)

     # FPN
     rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")						)
     rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")						)
     rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")						)
     for source_index, target_index in zip(range(3,							0,							-1						),							range(0,							3						)						):
          rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")						)
          rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")						)
          rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")						)
          rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")						)
          rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")						)
          rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")						)
     rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")						)
     rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")						)

     # Transformer decoder
     for idx in range(config.decoder_config.decoder_layers						):
          # self-attention out projection
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")						)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")						)
          # cross-attention out projection
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")						)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")						)
          # MLP 1
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")						)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")						)
          # MLP 2
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")						)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")						)
          # layernorm 1 (self-attention layernorm)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")						)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")						)
          # layernorm 2 (cross-attention layernorm)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")						)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")						)
          # layernorm 3 (final layernorm)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")						)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")						)

     rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")						)
     rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")						)

     # heads on top
     rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")						)

     rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")						)
     rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")						)

     rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")						)
     rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")						)

     for i in range(3						):
          rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")						)
          rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")						)
     # fmt: on

     return rename_keys






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase						):
     _SCREAMING_SNAKE_CASE							:						Tuple         =				dct.pop(SCREAMING_SNAKE_CASE__						)
     _SCREAMING_SNAKE_CASE							:						Dict         =				val






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase						):
     _SCREAMING_SNAKE_CASE							:						Tuple         =				[int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
     for i in range(len(backbone_config.depths						)						):
          _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				num_features[i]
          for j in range(backbone_config.depths[i]						):
               # fmt: off
               # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
               _SCREAMING_SNAKE_CASE							:						int         =				state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""						)
               _SCREAMING_SNAKE_CASE							:						Dict         =				state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""						)
               # next, add query, keys and values (in that order) to the state dict
               _SCREAMING_SNAKE_CASE							:						str         =				in_proj_weight[:dim, :]
               _SCREAMING_SNAKE_CASE							:						Any         =				in_proj_bias[: dim]
               _SCREAMING_SNAKE_CASE							:						Tuple         =				in_proj_weight[
                   dim : dim * 2, :
               ]
               _SCREAMING_SNAKE_CASE							:						int         =				in_proj_bias[
                   dim : dim * 2
               ]
               _SCREAMING_SNAKE_CASE							:						List[str]         =				in_proj_weight[
                   -dim :, :
               ]
               _SCREAMING_SNAKE_CASE							:						Any         =				in_proj_bias[-dim :]
               # fmt: on






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase						):
     _SCREAMING_SNAKE_CASE							:						Optional[int]         =				config.decoder_config.hidden_size
     for idx in range(config.decoder_config.decoder_layers						):
          # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
          _SCREAMING_SNAKE_CASE							:						Tuple         =				state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""						)
          _SCREAMING_SNAKE_CASE							:						Tuple         =				state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""						)
          # next, add query, keys and values (in that order) to the state dict
          _SCREAMING_SNAKE_CASE							:						str         =				in_proj_weight[: hidden_size, :]
          _SCREAMING_SNAKE_CASE							:						Dict         =				in_proj_bias[:config.hidden_size]
          _SCREAMING_SNAKE_CASE							:						List[Any]         =				in_proj_weight[hidden_size : hidden_size * 2, :]
          _SCREAMING_SNAKE_CASE							:						List[Any]         =				in_proj_bias[hidden_size : hidden_size * 2]
          _SCREAMING_SNAKE_CASE							:						Any         =				in_proj_weight[-hidden_size :, :]
          _SCREAMING_SNAKE_CASE							:						Optional[int]         =				in_proj_bias[-hidden_size :]
          # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
          _SCREAMING_SNAKE_CASE							:						Tuple         =				state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""						)
          _SCREAMING_SNAKE_CASE							:						Optional[int]         =				state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""						)
          # next, add query, keys and values (in that order) to the state dict
          _SCREAMING_SNAKE_CASE							:						Tuple         =				in_proj_weight[: hidden_size, :]
          _SCREAMING_SNAKE_CASE							:						Optional[int]         =				in_proj_bias[:config.hidden_size]
          _SCREAMING_SNAKE_CASE							:						Optional[int]         =				in_proj_weight[hidden_size : hidden_size * 2, :]
          _SCREAMING_SNAKE_CASE							:						Any         =				in_proj_bias[hidden_size : hidden_size * 2]
          _SCREAMING_SNAKE_CASE							:						List[str]         =				in_proj_weight[-hidden_size :, :]
          _SCREAMING_SNAKE_CASE							:						str         =				in_proj_bias[-hidden_size :]
     # fmt: on






def      lowerCamelCase__      ():
     _SCREAMING_SNAKE_CASE							:						int         =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
     _SCREAMING_SNAKE_CASE							:						str         =				Image.open(requests.get(SCREAMING_SNAKE_CASE__,							stream=SCREAMING_SNAKE_CASE__						).raw						)
     return im






@torch.no_grad()
def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase = False						):
     _SCREAMING_SNAKE_CASE							:						int         =				get_maskformer_config(SCREAMING_SNAKE_CASE__						)

     # load original state_dict
     with open(SCREAMING_SNAKE_CASE__,							"rb"						) as f:
          _SCREAMING_SNAKE_CASE							:						Dict         =				pickle.load(SCREAMING_SNAKE_CASE__						)
     _SCREAMING_SNAKE_CASE							:						List[Any]         =				data["""model"""]

     # for name, param in state_dict.items():
     #     print(name, param.shape)

     # rename keys
     _SCREAMING_SNAKE_CASE							:						Tuple         =				create_rename_keys(SCREAMING_SNAKE_CASE__						)
     for src, dest in rename_keys:
          rename_key(SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__						)
     read_in_swin_q_k_v(SCREAMING_SNAKE_CASE__,							config.backbone_config						)
     read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__						)

     # update to torch tensors
     for key, value in state_dict.items():
          _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				torch.from_numpy(SCREAMING_SNAKE_CASE__						)

     # load 🤗 model
     _SCREAMING_SNAKE_CASE							:						str         =				MaskFormerForInstanceSegmentation(SCREAMING_SNAKE_CASE__						)
     model.eval()

     for name, param in model.named_parameters():
          print(SCREAMING_SNAKE_CASE__,							param.shape						)

     _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				model.load_state_dict(SCREAMING_SNAKE_CASE__,							strict=SCREAMING_SNAKE_CASE__						)
     assert missing_keys == [
         "model.pixel_level_module.encoder.model.layernorm.weight",
         "model.pixel_level_module.encoder.model.layernorm.bias",
     ]
     assert len(SCREAMING_SNAKE_CASE__						) == 0, f"""Unexpected keys: {unexpected_keys}"""

     # verify results
     _SCREAMING_SNAKE_CASE							:						str         =				prepare_img()
     if "vistas" in model_name:
          _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				65
     elif "cityscapes" in model_name:
          _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				65535
     else:
          _SCREAMING_SNAKE_CASE							:						List[Any]         =				255
     _SCREAMING_SNAKE_CASE							:						List[str]         =				True if """ade""" in model_name else False
     _SCREAMING_SNAKE_CASE							:						Any         =				MaskFormerImageProcessor(ignore_index=SCREAMING_SNAKE_CASE__,							reduce_labels=SCREAMING_SNAKE_CASE__						)

     _SCREAMING_SNAKE_CASE							:						str         =				image_processor(SCREAMING_SNAKE_CASE__,							return_tensors="pt"						)

     _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				model(**SCREAMING_SNAKE_CASE__						)

     print("Logits:",							outputs.class_queries_logits[0, :3, :3]						)

     if model_name == "maskformer-swin-tiny-ade":
          _SCREAMING_SNAKE_CASE							:						str         =				torch.tensor(
              [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]						)
     assert torch.allclose(outputs.class_queries_logits[0, :3, :3],							SCREAMING_SNAKE_CASE__,							atol=1e-4						)
     print("Looks ok!"						)

     if pytorch_dump_folder_path is not None:
          print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""						)
          Path(SCREAMING_SNAKE_CASE__						).mkdir(exist_ok=SCREAMING_SNAKE_CASE__						)
          model.save_pretrained(SCREAMING_SNAKE_CASE__						)
          image_processor.save_pretrained(SCREAMING_SNAKE_CASE__						)

     if push_to_hub:
          print("Pushing model and image processor to the hub..."						)
          model.push_to_hub(f"""nielsr/{model_name}"""						)
          image_processor.push_to_hub(f"""nielsr/{model_name}"""						)


if __name__ == "__main__":
       UpperCamelCase__  			=argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--model_name',
           default='maskformer-swin-tiny-ade',
           type=str,
           help=('Name of the MaskFormer model you\'d like to convert',),
       )
       parser.add_argument(
           '--checkpoint_path',
           default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
           type=str,
           help='Path to the original state dict (.pth file).',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
       )
       parser.add_argument(
           '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
       )

       UpperCamelCase__  			=parser.parse_args()
       convert_maskformer_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )



'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_						:  str				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  Optional[int]				  =						{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: List[str]       =    """lilt"""






							def __init__(       self       ,   __snake_case=3_0522       ,   __snake_case=768       ,   __snake_case=12       ,   __snake_case=12       ,   __snake_case=3072       ,   __snake_case="gelu"       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=512       ,   __snake_case=2       ,   __snake_case=0.02       ,   __snake_case=1e-12       ,   __snake_case=0       ,   __snake_case="absolute"       ,   __snake_case=None       ,   __snake_case=4       ,   __snake_case=1024       ,   **__snake_case       ,   ):
											super().__init__(pad_token_id=__snake_case       ,   **__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= vocab_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= num_hidden_layers
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= num_attention_heads
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_act
											_SCREAMING_SNAKE_CASE					:			Dict							= intermediate_size
											_SCREAMING_SNAKE_CASE					:			Tuple							= hidden_dropout_prob
											_SCREAMING_SNAKE_CASE					:			List[str]							= attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= max_position_embeddings
											_SCREAMING_SNAKE_CASE					:			Dict							= type_vocab_size
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= initializer_range
											_SCREAMING_SNAKE_CASE					:			Tuple							= layer_norm_eps
											_SCREAMING_SNAKE_CASE					:			str							= position_embedding_type
											_SCREAMING_SNAKE_CASE					:			List[str]							= classifier_dropout
											_SCREAMING_SNAKE_CASE					:			int							= channel_shrink_ratio
											_SCREAMING_SNAKE_CASE					:			str							= max_ad_position_embeddings




